Boosting developer productivity: How Deloitte uses Amazon SageMaker Canvas for no-code/low-code machine learning

This article discusses how Deloitte utilizes AWS low-code and no-code machine learning services, such as Amazon SageMaker Canvas, to efficiently build and deploy ML models for clients and internal projects. It showcases the benefits of these services, including accessibility for non-programmers, rapid adoption of new technology, and cost-effective development.

Specifically, the article covers:

• An overview of Amazon SageMaker Canvas and its capabilities for data preparation, model building, and deployment
• A step-by-step demonstration of building a binary classification model for predicting loan defaults using SageMaker Canvas
• How SageMaker Canvas simplifies data preparation with features like Data Quality and Insights Report, one-hot encoding, and natural language prompts
• The process of building and deploying the model using SageMaker Canvas' visual interface
• Deloitte's experience in using these no-code/low-code tools to accelerate ML projects and improve productivity